question answering based on semantic graphs

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Question Answering Based on Semantic Graphs Lorand Dali – [email protected] Delia Rusu – [email protected] Blaž Fortuna – [email protected] Dunja Mladenić – [email protected] Marko Grobelnik – [email protected]

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Question Answering Based on Semantic Graphs. Lorand Dali – [email protected] Delia Rusu – [email protected] Bla ž Fortuna – [email protected] Dunja Mladeni ć – [email protected] Marko Grobelnik – [email protected]. Overview. Motivation System Overview Question Answering - PowerPoint PPT Presentation

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Page 1: Question Answering Based on Semantic Graphs

Question Answering Based on Semantic Graphs

Lorand Dali – [email protected] Rusu – [email protected]ž Fortuna – [email protected] Mladenić – [email protected] Grobelnik – [email protected]

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MotivationSystem OverviewQuestion AnsweringDocument Overview

FactsSemantic GraphDocument Summary

Conclusions

Overview

Page 3: Question Answering Based on Semantic Graphs

Motivation

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Motivation

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TripletsFacts stated in the textThe core of the sentence (subject, verb, object)

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System Overview

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Extract facts (triplets) from textIndex triplets to enable structured search on themAnalyze questions to obtain the queries for the triplet searchRetrieve the answer and the document containing itBrowse the document overview

Question Answering

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Question Answering

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Question types:Yes/No questions (Do animals eat fruit?),list questions (What do animals eat?),reason questions (Why do animals eat fruit?),quantity questions (How much fruit do animals eat?),location questions (Where do animals eat?) andtime questions (When do animals eat?).

Question Answering

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Analyze the document containing the answer:

Highlight facts described by subject – verb – object triplets (identified in the Penn Treebank parse tree)

Obtain the document semantic graph

View the automatic document summary

Document Overview

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Semantic GraphDocumentPlain text format

Named entity extractionCo-reference resolution

According to traditional Chinese medical belief, mental problems, laziness, malaria, epilepsy, toothache and lack of sexual appetite can be treated with tiger parts, leading to rampant poaching of the animal in Asia , the World Wide Fund ( WWF ) said.

AsiaWorld Wide Fund WWF

Asia - location

World Wide Fund - organization

WWF -organization

Co-reference

S – V – O triplet extraction

Triplet enhancement

Semantic Graph

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Document Summary

Feature Extractor

Features:linguisticdocumentgraph

Linear SVM

Linear Model

The Kerinci conservation project, an area of around three million hectares (7. 4 million acres) in west Sumatra, was being supported by funds from the World Bank, Subijanto said. [10.0912]Subijanto, a spokesman for the Forestry Ministry, said Indonesia was commited to protecting the tigers, which live within Sumatra's four designated conservation areas. [9.4155]

Rank

ing

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Document Summary

There are people wanting tiger products who didn't want them before, " Ron Lilley,coordinator for species conservation at the WWF in Jakarta, told Reuters.Subijanto, a spokesman for the Forestry Ministry, said Indonesia was commited to protecting the tigers, which live within Sumatra's four designated conservation areas.The Kerinci conservation project, an area of around three million hectares (7. 4 million acres) in west Sumatra, was being supported by funds from the World Bank, Subijanto said.

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Enhanced question answering systemQuestion answering, where the answer is supported by documentsDocument browsing

FactsDocument semantic graphAutomatic document summary

Conclusions

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Future workSystem extensions: triplet extraction, named entity recognitionExpand the search to look for answers in ontologiesRelax the requirement that the questions have a predefined formImprove the document overview functionality by integrating external resources

Conclusions

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Thank you!

Questions are guaranteed in life, answers aren’t.

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Extracted features:

Document Summary

Linguistic Attributes (13) Document Attributes (11)

Graph Attributes (9)

•Logical form tag•Treebank tag•Part of speech tag•Depth of linguistic node•8 semantic tags for named entities

•Sentence related: e.g. – location of sentence within doc•Triplet related: e.g.- frequency of triplet element in sentence, in doc, …

•Authority and Hub weight, Page Rank•Node degree•Size of weakly connected component•Size of max length chain•Frequency of verbs among edges

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Document SummaryObject - Word

Subject - Word

Verb - WordLocation Of Sentence In

DocumentSimilarity With CentroidNumber Of Locations In

SentenceNumber Of Named Entities In Sentence

Authority Weight Object

Hub Weight SubjectSize Weakly Conn Comp

Object

Rank

(Inf

orm

ation

Gai

n)